Psychophysics results: psychometric functions, visual weights and audiovisual variances. Psychophysics results: psychometric functions, visual weights.

Slides:



Advertisements
Similar presentations
Integration of sensory modalities
Advertisements

4.3 Fitted Effects for Factorial Data
The Positional Acuity of the Human Visual System Year 2 Practical Class Dr. Paul McGraw & Mr. Craig Stockdale.
Discrimination of time intervals presented in sequences marked by auditory signals delivered from different spatial sources Simon Grondin, Marilyn Plourde,
Evaluating Perceptual Cue Reliabilities Robert Jacobs Department of Brain and Cognitive Sciences University of Rochester.
Assumptions of Multiple Regression 1. Form of Relationship: –linear vs nonlinear –Main effects vs interaction effects 2. All relevant variables present.
Figure Figure up down rostral caudal V A’ up down rostral caudal A V A H 0 20 AVAV H 0.
Confidence Intervals.
Authors: Peter W. Battaglia, Robert A. Jacobs, and Richard N. Aslin
Figure 1 Experimental paradigm
Two Sample Tests When do use independent
The Method of Least-Squares
The “Flash-Lag” Effect Occurs in Audition and Cross-Modally
Regression model Y represents a value of the response variable.
Neuroscience: What You See and Hear Is What You Get
The Nose Smells What the Eye Sees
Choosing Goals, Not Rules: Deciding among Rule-Based Action Plans
Satoru Suzuki, Marcia Grabowecky  Neuron 
Volume 82, Issue 1, Pages (April 2014)
Linking Electrical Stimulation of Human Primary Visual Cortex, Size of Affected Cortical Area, Neuronal Responses, and Subjective Experience  Jonathan.
Graphing Techniques.
Integration of sensory modalities
Volume 34, Issue 5, Pages (May 2002)
Sergei Gepshtein, Martin S. Banks  Current Biology 
Volume 66, Issue 6, Pages (June 2010)
Volume 87, Issue 1, Pages (July 2015)
Perceptual Learning and Decision-Making in Human Medial Frontal Cortex
Michael L. Morgan, Gregory C. DeAngelis, Dora E. Angelaki  Neuron 
Volume 93, Issue 2, Pages (January 2017)
Ariel Zylberberg, Daniel M. Wolpert, Michael N. Shadlen  Neuron 
Hierarchical empirical Bayesian inference on group effects using the function spm_dcm_peb. Hierarchical empirical Bayesian inference on group effects using.
Hedging Your Bets by Learning Reward Correlations in the Human Brain
Young Children Do Not Integrate Visual and Haptic Form Information
Satoru Suzuki, Marcia Grabowecky  Neuron 
Part I Review Highlights, Chap 1, 2
Indicator Variables Response: Highway MPG
Simulation results with a bimodal basis function.
Neuronal Activity in Primate Dorsal Anterior Cingulate Cortex Signals Task Conflict and Predicts Adjustments in Pupil-Linked Arousal  R. Becket Ebitz,
Volume 34, Issue 5, Pages (May 2002)
Franco Pestilli, Marisa Carrasco, David J. Heeger, Justin L. Gardner 
Rethinking Motor Learning and Savings in Adaptation Paradigms: Model-Free Memory for Successful Actions Combines with Internal Models  Vincent S. Huang,
The Ventriloquist Effect Results from Near-Optimal Bimodal Integration
Opposite Effects of Recent History on Perception and Decision
ENM 310 Design of Experiments and Regression Analysis Chapter 3
Ryo Sasaki, Takanori Uka  Neuron  Volume 62, Issue 1, Pages (April 2009)
Active avoidance is impaired in aged mice in both multimodal and visual versions of the task. Active avoidance is impaired in aged mice in both multimodal.
Franco Pestilli, Marisa Carrasco, David J. Heeger, Justin L. Gardner 
A–E, Active avoidance (A–D) and optokinetic testing (E) in an independent cohort. A–E, Active avoidance (A–D) and optokinetic testing (E) in an independent.
Mathematical Foundations of BME Reza Shadmehr
Implicit adaptation generalizes around the aiming location (30° CCW)
One-Factor Experiments
Multisensory Integration and Attention in Developmental Dyslexia
Supervised Calibration Relies on the Multisensory Percept
When Correlation Implies Causation in Multisensory Integration
Altered development of small-worldness in pcdh19 mutants.
Developmental changes in transitivity in pcdh19 mutants.
Development of assortativity in wild-type and pcdh19 mutant larvae.
Discriminating IR light with visual cortex.
Repetition suppression: Interaction between trials without (prime) versus with (target) context (C) and repeated versus non-repeated context (i.e., repetition.
The Fisher information predicts the experimentally measured sensitivity. The Fisher information predicts the experimentally measured sensitivity. A, Sensitivity.
Christoph Kayser, Nikos K. Logothetis, Stefano Panzeri  Current Biology 
Volume 16, Issue 15, Pages (August 2006)
Incidental Processing of Biological Motion
Experimental procedures.
Cue-evoked dopamine release dynamics in the NAc shell and core.
Percent signal change in subcortical ROIs
Grand-averaged ERPs across (A) left hemisphere electrodes (P5, P7, P9, PO3, PO7, and O1) and (B) right hemisphere electrodes (P6, P8, P10, PO4, PO8, and.
Nonlinear transfer of signal and noise correlations in vivo.
Visual Crowding Is Correlated with Awareness
Problem 3.26, when assumptions are violated
Presentation transcript:

Psychophysics results: psychometric functions, visual weights and audiovisual variances. Psychophysics results: psychometric functions, visual weights and audiovisual variances. In audiovisual (AV) conditions, psychometric functions were fitted to the fraction of “right” location responses plotted as a function of the mean AV location. Data were fitted separately for audiovisual spatially congruent (△AV = 0°) and slightly conflicting conditions (△AV = ±6° with △AV = A - V). The empirical visual weight is computed from PSE locations of the audiovisual spatially conflicting psychometric functions (see equation 2). If the visual weight is >0.5, the PSE for △AV = -6° is left of the PSE for △AV = 6°. If the visual weight is smaller than 0.5, the PSE for △AV = -6° is right of the PSE for △AV = 6°. If the visual weight is equal to 0.5, the PSEs for △AV = -6° and △AV = 6° are identical. A–D, Psychometric functions for audiovisual spatially congruent and conflicting trials are plotted separately for the four conditions in our 2 (visual reliability: high, VR+ vs. low, VR-) x 2 (modality-specific report: auditory versus visual) factorial design. E, In unisensory conditions, psychometric functions were fitted to the fraction of “right” location responses plotted as a function of the signal location from unisensory auditory (A) and visual conditions of high (V, VR+) and low (V, VR-) visual reliability. F, Visual weights (mean ± SEM across participants): MLE predicted and empirical weights for the four conditions in our 2 (visual reliability: high, VR+ vs. low, VR-) x 2 (modality-specific report: auditory versus visual) factorial design. To facilitate the comparison with the MLE predictions that do not depend on modality-specific report, the visual weights are also plotted after pooling the data across both report conditions and re-fitting the neurometric functions. G, Standard deviations (σ, mean ± SEM across participants): Unisensory and audiovisual MLE-predicted and empirical standard deviations of the perceived spatial locations for the same combination of conditions as in F. For illustrational purposes, standard deviations were normalized by the auditory standard deviation (original auditory standard deviation = 39 ± 1.25; mean ± SEM). Tim Rohe, and Uta Noppeney eNeuro 2018;5:ENEURO.0315-17.2018 ©2018 by Society for Neuroscience